Item

Abstract

Accurate parameter estimation is key to maximizing the scientific impact of
gravitational-wave astronomy. Parameters of a binary merger are typically
estimated using Bayesian inference. It is necessary to make several assumptions
when doing so, one of which is that the the detectors output stationary
Gaussian noise. We test the validity of these assumptions by performing
percentile-percentile tests in both simulated Gaussian noise and real detector
data in the first observing run of Advanced LIGO (O1). We add simulated signals
to 512s of data centered on each of the three events detected in O1 --
GW150914, GW151012, and GW151226 -- and check that the recovered credible
intervals match statistical expectations. We find that we are able to recover
unbiased parameter estimates in the real detector data, indicating that the
assumption of Gaussian noise does not adversely effect parameter estimates.
However, we also find that both the parallel-tempered emcee sampler emcee_pt
and the nested sampler dynesty struggle to produced unbiased parameter
estimates for GW151226-like signals, even in simulated Gaussian noise. The
emcee_pt sampler does produce unbiased estimates for GW150914-like signals.
This highlights the importance of performing percentile-percentile tests in
different targeted areas of parameter space.